MCres package¶

Submodules¶

MCres.FakeSampler module¶

class MCres.FakeSampler.FakeSampler(flatchain, flatlnprobability)¶: Bases: object

MCres.MCres module¶

class MCres.MCres.MCres(sampler, paramstr=None, nwalker=None, niters=None, burnInIts=None, **kwargs)¶

Bases: object

Sampler can be the emcee sampler or a path to a ‘*.res.fits’ file paramstr is a list of (unique) string keywords that relate to each free parameter fitted in the emcee simulation. If left None, it will be set automatically to p1, p2, etc. nwalker, nwalker, burnInIts are the emcee MCMC simulation parameters

MCmap(param_x, param_y, bin_x=50, bin_y=50, cmap='jet', cm_min=None, cm_max=None, axescolor='w', polar=False, showmax=True, **kwargs)¶: Return a 2D histogram of the MC chain, showing the walker density per bin

Pbmap(param_x, param_y, bin_x=50, bin_y=50, cmap='jet', cm_min=None, cm_max=None, axescolor='w', polar=False, showmax=True, **kwargs)¶: Return a 2D histogram of the MC chain, showing the best loglikelihood per bin

addfilter(param, v_min=None, v_max=None)¶: Apply a filter on a parameter

best¶

bounds¶

chain2D¶

chainraw2D¶

corner(raw=False, bins=50, quantiles=[0.16, 0.5, 0.84], **kwargs)¶: Plot pretty corners for the whole simulation

delfilters()¶: Remove all visualization filters applied to the data

filters¶

fitparam(plot=False, params=[], perbin=131, q=[0.16, 0.84], best=0.5)¶: Fit all or specific parameter with a gaussian, because everything is gaussian

remfilter(ind)¶: Remove one visualization filter, of index ind in the filters list

save(name, clobber=False, append=False)¶

wrap(param, center=3.141592653589793, cycle=6.283185307179586)¶

MCres.load module¶

MCres.load.load(filename)¶

MCres.version module¶

Module contents¶

Easy stuff to process MCMC emcee results

class MCres.MCres(sampler, paramstr=None, nwalker=None, niters=None, burnInIts=None, **kwargs)¶